{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading /home/xiaoran/anaconda3/lib/python3.5/site-packages/matplotlib/mpl-data/sample_data/aapl.csv\n",
      "[ (datetime.date(2008, 10, 14), 116.26, 116.4, 103.14, 104.08, 70749800, 104.08)\n",
      " (datetime.date(2008, 10, 13), 104.55, 110.53, 101.02, 110.26, 54967000, 110.26)\n",
      " (datetime.date(2008, 10, 10), 85.7, 100.0, 85.0, 96.8, 79260700, 96.8)\n",
      " ...,\n",
      " (datetime.date(1984, 9, 11), 26.62, 27.37, 26.62, 26.87, 5444000, 3.07)\n",
      " (datetime.date(1984, 9, 10), 26.5, 26.62, 25.87, 26.37, 2346400, 3.01)\n",
      " (datetime.date(1984, 9, 7), 26.5, 26.87, 26.25, 26.5, 2981600, 3.02)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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SJFizpm3XioiISPekZFp6LDM47ri2J9M1NcGDhaee2vbXrKyEvn3hkktU7iEi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\n+9EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80baef5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.cbook as cbook\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "datafile = cbook.get_sample_data('aapl.csv', asfileobj=False)\n",
    "print('loading %s' % datafile)\n",
    "\n",
    "\n",
    "\n",
    "r = mlab.csv2rec(datafile)\n",
    "\n",
    "r.sort()\n",
    "r = r[-30:]  # get the last 30 days\n",
    "\n",
    "\n",
    "# first we'll do it the default way, with gaps on weekends\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
    "ax = axes[0]\n",
    "ax.plot(r.date, r.adj_close, 'o-')\n",
    "ax.set_title(\"Default\")\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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